{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 情感分析\n",
    "\n",
    "项目参考https://github.com/bentrevett/pytorch-sentiment-analysis\n",
    "\n",
    "在这个notebook项目中，用PyTorch和TorchText来做情感分析(检测一段文字的情感是正面的还是负面的)。我们会使用[IMDb 数据集](http://ai.stanford.edu/~amaas/data/sentiment/)，即电影评论。\n",
    "\n",
    "模型从简单到复杂，依次构建三种模型：\n",
    "- Word Averaging模型\n",
    "- RNN/LSTM模型\n",
    "- CNN模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第一步：导入豆瓣电影数据集，只有训练集和测试集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- TorchText中的一个重要概念是`Field`。`Field`决定了你的数据会被怎样处理。在我们的情感分类任务中，我们所需要接触到的数据有文本字符串和两种情感，\"pos\"或者\"neg\"。\n",
    "- `Field`的参数制定了数据会被怎样处理。\n",
    "- 我们使用`TEXT` field来定义如何处理电影评论，使用`LABEL` field来处理两个情感类别。\n",
    "- 我们的`TEXT` field带有`tokenize='spacy'`，这表示我们会用[spaCy](https://spacy.io) tokenizer来tokenize英文句子。如果我们不特别声明`tokenize`这个参数，那么默认的分词方法是使用空格。\n",
    "\n",
    "- `LABEL`由`LabelField`定义。这是一种特别的用来处理label的`Field`。我们后面会解释dtype。\n",
    "- 更多关于`Fields`，参见https://github.com/pytorch/text/blob/master/torchtext/data/field.py\n",
    "- 和之前一样，我们会设定random seeds使实验可以复现。\n",
    "\n",
    "\n",
    "- TorchText支持很多常见的自然语言处理数据集。\n",
    "- 下面的代码会自动下载IMDb数据集，然后分成train/test两个`torchtext.datasets`类别。数据被前面的`Fields`处理。IMDb数据集一共有50000电影评论，每个评论都被标注为正面的或负面的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color=red><b>先了解下Spacy库：[spaCy介绍和使用教程](https://juejin.im/post/5971a4b9f265da6c42353332?utm_source=gold_browser_extension%5D)</b></font>  \n",
    "<font color=red><b>再了解下torchtext库：[torchtext介绍和使用教程](https://blog.csdn.net/u012436149/article/details/79310176)：这个新手必看，不看下面代码听不懂</b></font> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torchtext import data\n",
    "\n",
    "\n",
    "SEED = 1234\n",
    "torch.manual_seed(SEED) #为CPU设置随机种子\n",
    "torch.cuda.manual_seed(SEED)#为GPU设置随机种子\n",
    "torch.backends.cudnn.deterministic = True  #可以提升一点训练速度，没有额外开销。\n",
    "\n",
    "#创建两个Field 对象\n",
    "TEXT = data.Field(tokenize='spacy',tokenizer_language='en_core_web_sm')\n",
    "#torchtext.data.Field : 用来定义字段的处理方法（文本字段，标签字段）\n",
    "LABEL = data.LabelField(dtype=torch.float)\n",
    "#LabelField是Field类的一个特殊子集，专门用于处理标签。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-4e6adfa7a8e8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtorchtext\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtrain_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdatasets\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mIMDB\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplits\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mTEXT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mLABEL\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mD:\\sorfware\\Anaconda3\\lib\\site-packages\\torchtext\\datasets\\imdb.py\u001b[0m in \u001b[0;36msplits\u001b[1;34m(cls, text_field, label_field, root, train, test, **kwargs)\u001b[0m\n\u001b[0;32m     53\u001b[0m         return super(IMDB, cls).splits(\n\u001b[0;32m     54\u001b[0m             \u001b[0mroot\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mroot\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtext_field\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtext_field\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel_field\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabel_field\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 55\u001b[1;33m             train=train, validation=None, test=test, **kwargs)\n\u001b[0m\u001b[0;32m     56\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     57\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\sorfware\\Anaconda3\\lib\\site-packages\\torchtext\\data\\dataset.py\u001b[0m in \u001b[0;36msplits\u001b[1;34m(cls, path, root, train, validation, test, **kwargs)\u001b[0m\n\u001b[0;32m     76\u001b[0m             \u001b[0mpath\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdownload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mroot\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     77\u001b[0m         train_data = None if train is None else cls(\n\u001b[1;32m---> 78\u001b[1;33m             os.path.join(path, train), **kwargs)\n\u001b[0m\u001b[0;32m     79\u001b[0m         val_data = None if validation is None else cls(\n\u001b[0;32m     80\u001b[0m             os.path.join(path, validation), **kwargs)\n",
      "\u001b[1;32mD:\\sorfware\\Anaconda3\\lib\\site-packages\\torchtext\\datasets\\imdb.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, path, text_field, label_field, **kwargs)\u001b[0m\n\u001b[0;32m     31\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;32min\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'pos'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'neg'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     32\u001b[0m             \u001b[1;32mfor\u001b[0m \u001b[0mfname\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mglob\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miglob\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'*.txt'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 33\u001b[1;33m                 \u001b[1;32mwith\u001b[0m \u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'r'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"utf-8\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     34\u001b[0m                     \u001b[0mtext\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m                 \u001b[0mexamples\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mExample\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfromlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mtext\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfields\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\sorfware\\Anaconda3\\lib\\codecs.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, errors)\u001b[0m\n\u001b[0;32m    307\u001b[0m     \u001b[0mbyte\u001b[0m \u001b[0msequences\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    308\u001b[0m     \"\"\"\n\u001b[1;32m--> 309\u001b[1;33m     \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'strict'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    310\u001b[0m         \u001b[0mIncrementalDecoder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    311\u001b[0m         \u001b[1;31m# undecoded input that is kept between calls to decode()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from torchtext import datasets\n",
    "train_data, test_data = datasets.IMDB.splits(TEXT, LABEL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(vars(train_data.examples[0])) #查看数据集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第二步：训练集划分为训练集和验证集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 由于我们现在只有train/test这两个分类，所以我们需要创建一个新的validation set。我们可以使用`.split()`创建新的分类。\n",
    "- 默认的数据分割是 70、30，如果我们声明`split_ratio`，可以改变split之间的比例，`split_ratio=0.8`表示80%的数据是训练集，20%是验证集。\n",
    "- 我们还声明`random_state`这个参数，确保我们每次分割的数据集都是一样的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "train_data, valid_data = train_data.split(random_state=random.seed(SEED)) #默认split_ratio=0.7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f'Number of training examples: {len(train_data)}')\n",
    "print(f'Number of validation examples: {len(valid_data)}')\n",
    "print(f'Number of testing examples: {len(test_data)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第三步：用训练集建立vocabulary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 下一步我们需要创建 _vocabulary_ 。_vocabulary_ 就是把每个单词一一映射到一个数字。\n",
    "![](assets/sentiment5.png)\n",
    "- 我们使用最常见的25k个单词来构建我们的单词表，用`max_size`这个参数可以做到这一点。\n",
    "- 所有其他的单词都用`<unk>`来表示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "TEXT.build_vocab(train_data, max_size=25000, vectors=\"glove.6B.100d\", unk_init=torch.Tensor.normal_)\n",
    "#从预训练的词向量（vectors） 中，将当前(corpus语料库)词汇表的词向量抽取出来，构成当前 corpus 的 Vocab（词汇表）。\n",
    "#预训练的 vectors 来自glove模型，每个单词有100维。glove模型训练的词向量参数来自很大的语料库，\n",
    "#而我们的电影评论的语料库小一点，所以词向量需要更新，glove的词向量适合用做初始化参数。\n",
    "LABEL.build_vocab(train_data) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f\"Unique tokens in TEXT vocabulary: {len(TEXT.vocab)}\")\n",
    "print(f\"Unique tokens in LABEL vocabulary: {len(LABEL.vocab)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(list(LABEL.vocab.stoi.items())) # 只有两个类别值\n",
    "print(list(TEXT.vocab.stoi.items())[:20])\n",
    "#语料库单词频率越高，索引越靠前。前两个默认为unk和pad。\n",
    "print(\"------\"*10)\n",
    "print(TEXT.vocab.freqs.most_common(20))\n",
    "# 这里可以看到unk和pad没有计数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(TEXT.vocab.itos[:10]) #查看TEXT单词表"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第四步：创建iterators，每个itartion都会返回一个batch的样本"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 最后一步数据的准备是创建iterators。每个itartion都会返回一个batch的examples。\n",
    "- 我们会使用`BucketIterator`。`BucketIterator`会把长度差不多的句子放到同一个batch中，确保每个batch中不出现太多的padding。\n",
    "- 严格来说，我们这份notebook中的模型代码都有一个问题，也就是我们把`<pad>`也当做了模型的输入进行训练。更好的做法是在模型中把由`<pad>`产生的输出给消除掉。在这节课中我们简单处理，直接把`<pad>`也用作模型输入了。由于`<pad>`数量不多，模型的效果也不差。\n",
    "- 如果我们有GPU，还可以指定每个iteration返回的tensor都在GPU上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "BATCH_SIZE = 64\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "#相当于把样本划分batch，把相等长度的单词尽可能的划分到一个batch，不够长的就用padding。\n",
    "train_iterator, valid_iterator, test_iterator = data.BucketIterator.splits(\n",
    "                                                        (train_data, valid_data, test_data), \n",
    "                                                        batch_size=BATCH_SIZE,\n",
    "                                                        device=device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(next(iter(train_iterator)).label.shape)\n",
    "print(next(iter(train_iterator)).text.shape)  \n",
    "# 多运行一次可以发现一条评论的单词长度会变\n",
    "# 下面text的维度983*64，983为一条评论的单词长度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 取出一句评论\n",
    "batch = next(iter(train_iterator))\n",
    "print(batch.text.shape) \n",
    "print([TEXT.vocab.itos[i] for i in batch.text[:,0]])\n",
    "# 可以看到这句话的长度是1077，最后面有很多pad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第五步：创建Word Averaging模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Word Averaging模型\n",
    "\n",
    "- 我们首先介绍一个简单的Word Averaging模型。这个模型非常简单，我们把每个单词都通过`Embedding`层投射成word embedding vector，然后把一句话中的所有word vector做个平均，就是整个句子的vector表示了。接下来把这个sentence vector传入一个`Linear`层，做分类即可。\n",
    "\n",
    "![](assets/sentiment8.png)\n",
    "\n",
    "- 我们使用[`avg_pool2d`](https://pytorch.org/docs/stable/nn.html?highlight=avg_pool2d#torch.nn.functional.avg_pool2d)来做average pooling。我们的目标是把sentence length那个维度平均成1，然后保留embedding这个维度。\n",
    "\n",
    "![](assets/sentiment9.png)\n",
    "\n",
    "- `avg_pool2d`的kernel size是 (`embedded.shape[1]`, 1)，所以句子长度的那个维度会被压扁。\n",
    "\n",
    "![](assets/sentiment10.png)\n",
    "\n",
    "![](assets/sentiment11.png)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "\n",
    "class WordAVGModel(nn.Module):\n",
    "    def __init__(self, vocab_size, embedding_dim, output_dim, pad_idx):\n",
    "        super().__init__()\n",
    "        self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=pad_idx)\n",
    "        #vocab_size=25002，embedding_dim=100，padding_idx：遇到pad的单词用0填充     \n",
    "        self.fc = nn.Linear(embedding_dim, output_dim) #output_dim=1\n",
    "        \n",
    "    def forward(self, text): # text: [seq_len,batch_size]\n",
    "        embedded = self.embedding(text)  # embedded = [seq_len, batch_size, embedding_dim] \n",
    "        embedded = embedded.permute(1, 0, 2) # [batch_size, seq_len, embedding_dim]\n",
    "        \n",
    "        pooled = F.avg_pool2d(embedded, (embedded.shape[1], 1)).squeeze(1)   \n",
    "        # kernel size=(embedded.shape[1], 1), 会将句子的长度压缩到1，[batch_size, 1, embedding_dim]最终将每句话用一个向量表示\n",
    "        # [batch_size, embedding_dim] 把句子长度的维度压扁为1，并降维\n",
    "        \n",
    "        return self.fc(pooled)  # batch_size, output_dim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "VOCAB_SZIE = len(TEXT.vocab)\n",
    "EMBEDDING_DIM = 100\n",
    "OUTPUT_DIM = 1 \n",
    "PAD_IDX = TEXT.vocab.stoi[TEXT.pad_token] \n",
    "\n",
    "model = WordAVGModel(VOCAB_SZIE, EMBEDDING_DIM, OUTPUT_DIM, PAD_IDX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def count_parameters(model):\n",
    "    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n",
    "\n",
    "print(f'The model has {count_parameters(model):,} trainable parameters')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第六步：初始化参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把模型参数初始化成glove的向量参数\n",
    "pretrained_embeddings = TEXT.vocab.vectors  # 取出glove embedding词向量的参数\n",
    "model.embedding.weight.data.copy_(pretrained_embeddings) #遇到_的语句直接替换，不需要另外赋值=\n",
    "#把上面vectors=\"glove.6B.100d\"取出的词向量作为初始化参数，数量为25000*100个参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "UNK_IDX = TEXT.vocab.stoi[TEXT.unk_token] # UNK_IDX=0\n",
    "\n",
    "model.embedding.weight.data[UNK_IDX] = torch.zeros(EMBEDDING_DIM) \n",
    "model.embedding.weight.data[PAD_IDX] = torch.zeros(EMBEDDING_DIM)\n",
    "#词汇表25002个单词，前两个unk和pad也需要初始化成EMBEDDING_DIM维的向量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第七步：训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch.optim as optim\n",
    "\n",
    "optimizer = optim.Adam(model.parameters()) \n",
    "loss_fn = nn.BCEWithLogitsLoss()  #BCEWithLogitsLoss：二分类损失函数\n",
    "model = model.to(device) \n",
    "loss_fn = loss_fn.to(device) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def binary_accuracy(preds, y): \n",
    "    '''计算准确度，即预测和实际标签的相匹配的个数'''\n",
    "    rounded_preds = torch.round(torch.sigmoid(preds)) #.round函数：四舍五入[neg: 0, pos: 1]\n",
    "    correct = (rounded_preds == y).float() #convert into float for division \n",
    "    acc = correct.sum()/len(correct)\n",
    "    return acc\n",
    "\n",
    "\n",
    "def train(model, iterator, optimizer, loss_fn):\n",
    "    epoch_loss = 0\n",
    "    epoch_acc = 0\n",
    "    total_len = 0\n",
    "    model.train()  #训练时会用到dropout、归一化等方法，但测试的时候不能用dropout等方法\n",
    "    \n",
    "    for batch in iterator: \n",
    "        optimizer.zero_grad() \n",
    "        preds = model(batch.text).squeeze(1) # squeeze(1)压缩维度，和batch.label维度对上 \n",
    "        loss = loss_fn(preds, batch.label)\n",
    "        acc = binary_accuracy(preds, batch.label)\n",
    "             \n",
    "        loss.backward() \n",
    "        optimizer.step() \n",
    "        \n",
    "        epoch_loss += loss.item() * len(batch.label)\n",
    "        #得到一个batch累加得到所有样本损失\n",
    "        \n",
    "        epoch_acc += acc.item() * len(batch.label)\n",
    "        #（acc.item()：一个batch的正确率） *batch数 = 正确数, 累加得到所有训练样本正确数。\n",
    "        \n",
    "        total_len += len(batch.label)\n",
    "        #计算train_iterator所有样本的数量，不出意外应该是17500\n",
    "        \n",
    "    return epoch_loss / total_len, epoch_acc / total_len #得到所有的batch的平均loss和平均acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate(model, iterator, loss_fn):\n",
    "    epoch_loss = 0\n",
    "    epoch_acc = 0\n",
    "    total_len = 0\n",
    "    model.eval()\n",
    "    #转换成测试模式，冻结dropout层或其他层。\n",
    "    \n",
    "    with torch.no_grad():  \n",
    "        for batch in iterator: \n",
    "            \n",
    "            preds = model(batch.text).squeeze(1)\n",
    "            loss = loss_fn(preds, batch.label)\n",
    "            acc = binary_accuracy(preds, batch.label)\n",
    "            \n",
    "            epoch_loss += loss.item() * len(batch.label)\n",
    "            epoch_acc += acc.item() * len(batch.label)\n",
    "            total_len += len(batch.label)\n",
    "    model.train()\n",
    "    return epoch_loss / total_len, epoch_acc / total_len"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time \n",
    "\n",
    "def epoch_time(start_time, end_time): \n",
    "    elapsed_time = end_time - start_time\n",
    "    elapsed_mins = int(elapsed_time / 60)\n",
    "    elapsed_secs = int(elapsed_time - (elapsed_mins * 60))\n",
    "    return elapsed_mins, elapsed_secs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第八步：查看模型运行结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# kaggleGPU跑的花了2分钟\n",
    "N_EPOCHS = 20\n",
    "best_valid_loss = float('inf') \n",
    "\n",
    "for epoch in range(N_EPOCHS):\n",
    "    start_time = time.time()\n",
    "    \n",
    "    train_loss, train_acc = train(model, train_iterator, optimizer, loss_fn) # 得到训练集每个epoch的平均损失和准确率\n",
    "    valid_loss, valid_acc = evaluate(model, valid_iterator, loss_fn) # 验证集每个epoch的平均损失和准确率，model传入的是训练完的参数\n",
    "    \n",
    "    end_time = time.time()\n",
    "    epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n",
    "    \n",
    "    if valid_loss < best_valid_loss: \n",
    "        best_valid_loss = valid_loss\n",
    "        torch.save(model.state_dict(), 'wordavg-model.pth')\n",
    "    \n",
    "    print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')\n",
    "    print(f'\\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')\n",
    "    print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. Acc: {valid_acc*100:.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第九步：预测结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import HTML\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "def create_download_link(title = \"Download model file\", filename = \"CNN-model.pt\"):  \n",
    "    html = '<a href={filename}>{title}</a>'\n",
    "    html = html.format(title=title,filename=filename)\n",
    "    return HTML(html)\n",
    "\n",
    "# create a link to download the dataframe which was saved with .to_csv method\n",
    "create_download_link(filename='wordavg-model.pth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'model' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-f795a3e78d6a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"wordavg-model.pt\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;31m#用保存的模型参数预测数据\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
     ]
    }
   ],
   "source": [
    "model.load_state_dict(torch.load(\"wordavg-model.pth\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import spacy  \n",
    "nlp = spacy.load('en_core_web_sm') \n",
    "\n",
    "def predict_sentiment(sentence):\n",
    "    tokenized = [tok.text for tok in nlp.tokenizer(sentence)] \n",
    "    # print(tokenized) = ['I', 'love', 'This', 'film', 'bad']\n",
    "    indexed = [TEXT.vocab.stoi[t] for t in tokenized] \n",
    "    \n",
    "    tensor = torch.LongTensor(indexed).to(device) #seq_len\n",
    "    # 所有词向量都应该变成LongTensor\n",
    "    \n",
    "    tensor = tensor.unsqueeze(1) \n",
    "    #模型的输入是[seq_len, batch_size],这里batch_size为1\n",
    "    \n",
    "    pred = torch.sigmoid(model(tensor))\n",
    "    # 预测准确率，在0，1之间，需要sigmoid下\n",
    "    \n",
    "    return pred.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9373546242713928"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_sentiment(\"I love This film bad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_sentiment(\"This film is great\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RNN模型\n",
    "\n",
    "- 下面我们尝试把模型换成一个**recurrent neural network** (RNN)。RNN经常会被用来encode一个sequence\n",
    "$$h_t = \\text{RNN}(x_t, h_{t-1})$$\n",
    "- 我们使用最后一个hidden state $h_T$来表示整个句子。\n",
    "- 然后我们把$h_T$通过一个线性变换$f$，然后用来预测句子的情感。\n",
    "\n",
    "![](assets/sentiment1.png)\n",
    "\n",
    "![](assets/sentiment7.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "双向RNN网络\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RNN(nn.Module):\n",
    "    def __init__(self, vocab_size, embedding_dim, hidden_dim, output_dim, n_layers, bidirectional, dropout, pad_idx):\n",
    "        super().__init__()\n",
    "        self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=pad_idx)\n",
    "        self.lstm = nn.LSTM(embedding_dim, hidden_dim, num_layers=n_layers, bidirectional=bidirectional, dropout=dropout)\n",
    "        self.fc = nn.Linear(hidden_size*2, output_size)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "    def forward(self, text): # text=[seq_len, batch_size]\n",
    "        embedded = self.dropout(self.embedding(text)) #[seq_len, batch_size, embedding_dim]\n",
    "        output, (hidden, cell) = self.lstm(embedded)\n",
    "        # output = [seq_len, batch_size, hidden_dim * num directions]\n",
    "        # hidden = cell = [num layers * num directions, batch_size, hidden_dim]\n",
    "        \n",
    "        # 这里的num layers * num directions可以看上面图，上面图除掉输入输出层只有两层双向网络。\n",
    "        # num layers = 2表示需要纵向上在加两层双向，总共有4层神经元,只需要最后一层的双向lstm即可\n",
    "        #concat the final forward (hidden[-2,:,:]) and backward (hidden[-1,:,:]) hidden layers\n",
    "        hidden = self.dropout(torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim=1)) \n",
    "        # hidden = [batch_size, hidden_dim * num directions]\n",
    "        # 看下上面图示，最后前向和后向输出的隐藏层会concat到输出层，4层神经元最后两层作为最终的输出。\n",
    "        # 这里因为我们只需要得到最后一个时间序列的输出，所以最终输出的hidden跟seq_len无关。\n",
    "        \n",
    "        return self.fc(hidden.squeeze(0)) # #sequeeze()只有维度为1才会去掉，输出[batch size, output_dim]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RNN(\n",
       "  (embedding): Embedding(25002, 100, padding_idx=1)\n",
       "  (rnn): LSTM(100, 256, num_layers=2, dropout=0.5, bidirectional=True)\n",
       "  (fc): Linear(in_features=512, out_features=1, bias=True)\n",
       "  (dropout): Dropout(p=0.5)\n",
       ")"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VOCAB_SIZE = len(TEXT.vocab)\n",
    "EMBEDDING_DIM = 100\n",
    "HIDDEN_DIM = 256\n",
    "OUTPUT_DIM = 1\n",
    "N_LAYERS = 2\n",
    "BIDIRECTIONAL = True\n",
    "DROPOUT = 0.5\n",
    "PAD_IDX = TEXT.vocab.stoi[TEXT.pad_token]\n",
    "\n",
    "model = RNN(VOCAB_SIZE, EMBEDDING_DIM, HIDDEN_DIM, OUTPUT_DIM, N_LAYERS, BIDIRECTIONAL, DROPOUT, PAD_IDX)\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model has 4,810,857 trainable parameters\n"
     ]
    }
   ],
   "source": [
    "print(f'The model has {count_parameters(model):,} trainable parameters')\n",
    "# 比averge model模型多了一倍的参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([[ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
      "        [ 0.0000,  0.0000,  0.0000,  ...,  0.0000,  0.0000,  0.0000],\n",
      "        [-0.0382, -0.2449,  0.7281,  ..., -0.1459,  0.8278,  0.2706],\n",
      "        ...,\n",
      "        [-0.1419,  0.0282,  0.2185,  ..., -0.1100, -0.1250,  0.0282],\n",
      "        [-0.3326, -0.9215,  0.9239,  ...,  0.5057, -1.2898,  0.1782],\n",
      "        [-0.8304,  0.3732,  0.0726,  ..., -0.0122,  0.2313, -0.2783]])\n"
     ]
    }
   ],
   "source": [
    "# 同上初始化\n",
    "model.embedding.weight.data.copy_(pretrained_embeddings)\n",
    "UNK_IDX = TEXT.vocab.stoi[TEXT.unk_token]\n",
    "\n",
    "model.embedding.weight.data[UNK_IDX] = torch.zeros(EMBEDDING_DIM)\n",
    "model.embedding.weight.data[PAD_IDX] = torch.zeros(EMBEDDING_DIM)\n",
    "\n",
    "print(model.embedding.weight.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 训练RNN模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = optim.Adam(model.parameters())\n",
    "model = model.to(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 01 | Epoch Time: 2m 1s\n",
      "\tTrain Loss: 0.667 | Train Acc: 59.09%\n",
      "\t Val. Loss: 0.633 |  Val. Acc: 64.67%\n",
      "Epoch: 02 | Epoch Time: 2m 1s\n",
      "\tTrain Loss: 0.663 | Train Acc: 60.33%\n",
      "\t Val. Loss: 0.669 |  Val. Acc: 69.21%\n",
      "Epoch: 03 | Epoch Time: 2m 2s\n",
      "\tTrain Loss: 0.650 | Train Acc: 61.06%\n",
      "\t Val. Loss: 0.579 |  Val. Acc: 70.55%\n",
      "Epoch: 04 | Epoch Time: 2m 2s\n",
      "\tTrain Loss: 0.493 | Train Acc: 77.43%\n",
      "\t Val. Loss: 0.382 |  Val. Acc: 83.43%\n",
      "Epoch: 05 | Epoch Time: 2m 2s\n",
      "\tTrain Loss: 0.394 | Train Acc: 83.71%\n",
      "\t Val. Loss: 0.338 |  Val. Acc: 85.97%\n",
      "Epoch: 06 | Epoch Time: 2m 3s\n",
      "\tTrain Loss: 0.338 | Train Acc: 86.26%\n",
      "\t Val. Loss: 0.309 |  Val. Acc: 87.21%\n",
      "Epoch: 07 | Epoch Time: 2m 2s\n",
      "\tTrain Loss: 0.292 | Train Acc: 88.37%\n",
      "\t Val. Loss: 0.295 |  Val. Acc: 88.73%\n",
      "Epoch: 08 | Epoch Time: 2m 3s\n",
      "\tTrain Loss: 0.252 | Train Acc: 90.26%\n",
      "\t Val. Loss: 0.300 |  Val. Acc: 89.31%\n",
      "Epoch: 09 | Epoch Time: 2m 2s\n",
      "\tTrain Loss: 0.246 | Train Acc: 90.51%\n",
      "\t Val. Loss: 0.282 |  Val. Acc: 88.76%\n",
      "Epoch: 10 | Epoch Time: 2m 3s\n",
      "\tTrain Loss: 0.205 | Train Acc: 92.37%\n",
      "\t Val. Loss: 0.295 |  Val. Acc: 88.31%\n",
      "Epoch: 11 | Epoch Time: 2m 1s\n",
      "\tTrain Loss: 0.203 | Train Acc: 92.46%\n",
      "\t Val. Loss: 0.289 |  Val. Acc: 89.25%\n",
      "Epoch: 12 | Epoch Time: 2m 3s\n",
      "\tTrain Loss: 0.178 | Train Acc: 93.58%\n",
      "\t Val. Loss: 0.301 |  Val. Acc: 89.41%\n",
      "Epoch: 13 | Epoch Time: 2m 3s\n",
      "\tTrain Loss: 0.158 | Train Acc: 94.43%\n",
      "\t Val. Loss: 0.301 |  Val. Acc: 89.51%\n",
      "Epoch: 14 | Epoch Time: 2m 2s\n",
      "\tTrain Loss: 0.158 | Train Acc: 94.63%\n",
      "\t Val. Loss: 0.289 |  Val. Acc: 89.95%\n",
      "Epoch: 15 | Epoch Time: 2m 2s\n",
      "\tTrain Loss: 0.142 | Train Acc: 95.00%\n",
      "\t Val. Loss: 0.314 |  Val. Acc: 89.59%\n",
      "Epoch: 16 | Epoch Time: 2m 2s\n",
      "\tTrain Loss: 0.123 | Train Acc: 95.62%\n",
      "\t Val. Loss: 0.329 |  Val. Acc: 89.99%\n",
      "Epoch: 17 | Epoch Time: 2m 4s\n",
      "\tTrain Loss: 0.107 | Train Acc: 96.16%\n",
      "\t Val. Loss: 0.325 |  Val. Acc: 89.75%\n",
      "Epoch: 18 | Epoch Time: 2m 4s\n",
      "\tTrain Loss: 0.100 | Train Acc: 96.66%\n",
      "\t Val. Loss: 0.341 |  Val. Acc: 89.49%\n",
      "Epoch: 19 | Epoch Time: 2m 3s\n",
      "\tTrain Loss: 0.096 | Train Acc: 96.63%\n",
      "\t Val. Loss: 0.340 |  Val. Acc: 89.79%\n",
      "Epoch: 20 | Epoch Time: 2m 3s\n",
      "\tTrain Loss: 0.080 | Train Acc: 97.31%\n",
      "\t Val. Loss: 0.380 |  Val. Acc: 89.83%\n"
     ]
    }
   ],
   "source": [
    "# 用kaggleGPU跑花了40分钟。\n",
    "N_EPOCHS = 20\n",
    "best_valid_loss = float('inf')\n",
    "\n",
    "for epoch in range(N_EPOCHS):\n",
    "    start_time = time.time()\n",
    "    train_loss, train_acc = train(model, train_iterator, optimizer, criterion)\n",
    "    valid_loss, valid_acc = evaluate(model, valid_iterator, criterion)\n",
    "    \n",
    "    end_time = time.time()\n",
    "    epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n",
    "    \n",
    "    if valid_loss < best_valid_loss:\n",
    "        best_valid_loss = valid_loss\n",
    "        torch.save(model.state_dict(), 'lstm-model.pt')\n",
    "    \n",
    "    print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')\n",
    "    print(f'\\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')\n",
    "    print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. Acc: {valid_acc*100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href=lstm-model.pt>Download model file</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 下载文件到本地\n",
    "from IPython.display import HTML\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "def create_download_link(title = \"Download model file\", filename = \"wordavg-model.pt\"):  \n",
    "    html = '<a href={filename}>{title}</a>'\n",
    "    html = html.format(title=title,filename=filename)\n",
    "    return HTML(html)\n",
    "\n",
    "# create a link to download the dataframe which was saved with .to_csv method\n",
    "create_download_link(filename='lstm-model.pt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.304 | Test Acc: 88.11%\n"
     ]
    }
   ],
   "source": [
    "model.load_state_dict(torch.load('lstm-model.pt'))\n",
    "test_loss, test_acc = evaluate(model, test_iterator, criterion)\n",
    "print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3637591600418091"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_sentiment(\"I feel This film bad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9947803020477295"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_sentiment(\"This film is great\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CNN模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CNN(nn.Module):\n",
    "    def __init__(self, vocab_size, embedding_dim, n_filters, filter_sizes, output_dim, dropout, pad_idx):\n",
    "        super().__init__()\n",
    "        self.embedding = nn.Embedding(vocab_size, embedding_dim, padding_idx=pad_idx)\n",
    "        self.convs = nn.ModuleList([nn.Conv2d(in_channels = 1, out_channels = n_filters, \n",
    "                                    kernel_size = (fs, embedding_dim)) for fs in filter_sizes])\n",
    "        # in_channels：输入的channel，文字都是1\n",
    "        # out_channels：输出的channel维度\n",
    "        # fs：每次滑动窗口计算用到几个单词\n",
    "        # for fs in filter_sizes打算用好几个卷积模型最后concate起来看效果。\n",
    "        \n",
    "        self.fc = nn.Linear(len(filter_sizes) * n_filters, output_dim)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        \n",
    "    def forward(self, text):\n",
    "        text = text.permute(1, 0) # [batch size, sent len]\n",
    "        embedded = self.embedding(text) # [batch size, sent len, emb dim]\n",
    "        embedded = embedded.unsqueeze(1) # [batch size, 1, sent len, emb dim]\n",
    "        # 升维是为了和nn.Conv2d的输入维度吻合，把channel列升维。\n",
    "        conved = [F.relu(conv(embedded)).squeeze(3) for conv in self.convs]\n",
    "        # conved = [batch size, n_filters, sent len - filter_sizes+1]\n",
    "        # 有几个filter_sizes就有几个conved\n",
    "    \n",
    "        pooled = [F.max_pool1d(conv, conv.shape[2]).squeeze(2) for conv in conved]\n",
    "        # 把conv的第三个维度最大池化了\n",
    "        #pooled_n = [batch size, n_filters]\n",
    "        \n",
    "        cat = self.dropout(torch.cat(pooled, dim=1))\n",
    "        # cat = [batch size, n_filters * len(filter_sizes)]\n",
    "        # 把 len(filter_sizes)个卷积模型concate起来传到全连接层。\n",
    "            \n",
    "        return self.fc(cat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model has 2,620,801 trainable parameters\n"
     ]
    }
   ],
   "source": [
    "VOCAB_SIZE = len(TEXT.vocab)\n",
    "EMBEDDING_DIM = 100\n",
    "N_FILTERS = 100\n",
    "FILTER_SIZES = [3,4,5]\n",
    "OUTPUT_DIM = 1\n",
    "DROPOUT = 0.5\n",
    "PAD_IDX = TEXT.vocab.stoi[TEXT.pad_token]\n",
    "\n",
    "model = CNN(VOCAB_SIZE, EMBEDDING_DIM, N_FILTERS, FILTER_SIZES, OUTPUT_DIM, DROPOUT, PAD_IDX)\n",
    "model.embedding.weight.data.copy_(pretrained_embeddings)\n",
    "UNK_IDX = TEXT.vocab.stoi[TEXT.unk_token]\n",
    "\n",
    "model.embedding.weight.data[UNK_IDX] = torch.zeros(EMBEDDING_DIM)\n",
    "model.embedding.weight.data[PAD_IDX] = torch.zeros(EMBEDDING_DIM)\n",
    "model = model.to(device)\n",
    "print(f'The model has {count_parameters(model):,} trainable parameters')\n",
    "# 比averge model模型参数差不多"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 01 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 0.652 | Train Acc: 61.81%\n",
      "\t Val. Loss: 0.527 |  Val. Acc: 76.20%\n",
      "Epoch: 02 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 0.427 | Train Acc: 80.66%\n",
      "\t Val. Loss: 0.358 |  Val. Acc: 84.36%\n",
      "Epoch: 03 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 0.304 | Train Acc: 87.14%\n",
      "\t Val. Loss: 0.318 |  Val. Acc: 86.45%\n",
      "Epoch: 04 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 0.215 | Train Acc: 91.42%\n",
      "\t Val. Loss: 0.313 |  Val. Acc: 86.92%\n",
      "Epoch: 05 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 0.156 | Train Acc: 94.18%\n",
      "\t Val. Loss: 0.326 |  Val. Acc: 87.01%\n",
      "Epoch: 06 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 0.105 | Train Acc: 96.33%\n",
      "\t Val. Loss: 0.344 |  Val. Acc: 87.16%\n",
      "Epoch: 07 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 0.075 | Train Acc: 97.61%\n",
      "\t Val. Loss: 0.372 |  Val. Acc: 87.28%\n",
      "Epoch: 08 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 0.052 | Train Acc: 98.39%\n",
      "\t Val. Loss: 0.403 |  Val. Acc: 87.21%\n",
      "Epoch: 09 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 0.041 | Train Acc: 98.64%\n",
      "\t Val. Loss: 0.433 |  Val. Acc: 87.09%\n",
      "Epoch: 10 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 0.031 | Train Acc: 99.10%\n",
      "\t Val. Loss: 0.462 |  Val. Acc: 87.01%\n",
      "Epoch: 11 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 0.023 | Train Acc: 99.29%\n",
      "\t Val. Loss: 0.495 |  Val. Acc: 86.93%\n",
      "Epoch: 12 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 0.021 | Train Acc: 99.34%\n",
      "\t Val. Loss: 0.530 |  Val. Acc: 86.84%\n",
      "Epoch: 13 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 0.015 | Train Acc: 99.60%\n",
      "\t Val. Loss: 0.559 |  Val. Acc: 86.73%\n",
      "Epoch: 14 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 0.013 | Train Acc: 99.69%\n",
      "\t Val. Loss: 0.597 |  Val. Acc: 86.48%\n",
      "Epoch: 15 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 0.012 | Train Acc: 99.70%\n",
      "\t Val. Loss: 0.608 |  Val. Acc: 86.63%\n",
      "Epoch: 16 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 0.009 | Train Acc: 99.76%\n",
      "\t Val. Loss: 0.640 |  Val. Acc: 86.77%\n",
      "Epoch: 17 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 0.010 | Train Acc: 99.73%\n",
      "\t Val. Loss: 0.674 |  Val. Acc: 86.51%\n",
      "Epoch: 18 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 0.012 | Train Acc: 99.63%\n",
      "\t Val. Loss: 0.704 |  Val. Acc: 86.71%\n",
      "Epoch: 19 | Epoch Time: 0m 19s\n",
      "\tTrain Loss: 0.010 | Train Acc: 99.65%\n",
      "\t Val. Loss: 0.757 |  Val. Acc: 86.44%\n",
      "Epoch: 20 | Epoch Time: 0m 20s\n",
      "\tTrain Loss: 0.006 | Train Acc: 99.80%\n",
      "\t Val. Loss: 0.756 |  Val. Acc: 86.55%\n"
     ]
    }
   ],
   "source": [
    "# 同上，需要花8分钟左右\n",
    "optimizer = optim.Adam(model.parameters())\n",
    "criterion = nn.BCEWithLogitsLoss()\n",
    "criterion = criterion.to(device)\n",
    "\n",
    "N_EPOCHS = 20\n",
    "\n",
    "best_valid_loss = float('inf')\n",
    "\n",
    "for epoch in range(N_EPOCHS):\n",
    "\n",
    "    start_time = time.time()\n",
    "    \n",
    "    train_loss, train_acc = train(model, train_iterator, optimizer, criterion)\n",
    "    valid_loss, valid_acc = evaluate(model, valid_iterator, criterion)\n",
    "    \n",
    "    end_time = time.time()\n",
    "    epoch_mins, epoch_secs = epoch_time(start_time, end_time)\n",
    "    \n",
    "    if valid_loss < best_valid_loss:\n",
    "        best_valid_loss = valid_loss\n",
    "        torch.save(model.state_dict(), 'CNN-model.pt')\n",
    "    \n",
    "    print(f'Epoch: {epoch+1:02} | Epoch Time: {epoch_mins}m {epoch_secs}s')\n",
    "    print(f'\\tTrain Loss: {train_loss:.3f} | Train Acc: {train_acc*100:.2f}%')\n",
    "    print(f'\\t Val. Loss: {valid_loss:.3f} |  Val. Acc: {valid_acc*100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Loss: 0.339 | Test Acc: 85.68%\n"
     ]
    }
   ],
   "source": [
    "# 发现上面结果过拟合了，同学们可以自行调参\n",
    "model.load_state_dict(torch.load('CNN-model.pt'))\n",
    "test_loss, test_acc = evaluate(model, test_iterator, criterion)\n",
    "print(f'Test Loss: {test_loss:.3f} | Test Acc: {test_acc*100:.2f}%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6535547375679016"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_sentiment(\"I feel This film bad\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9950380921363831"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_sentiment(\"This film is great well\") \n",
    "# 我后面加了个well，不加会报错，因为我们的FILTER_SIZES = [3,4,5]有设置为5，所以输出的句子长度不能小于5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<a href=CNN-model.pt>Download model file</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# kaggle上下载模型文件到本地\n",
    "from IPython.display import HTML\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "def create_download_link(title = \"Download model file\", filename = \"CNN-model.pt\"):  \n",
    "    html = '<a href={filename}>{title}</a>'\n",
    "    html = html.format(title=title,filename=filename)\n",
    "    return HTML(html)\n",
    "\n",
    "# create a link to download the dataframe which was saved with .to_csv method\n",
    "create_download_link(filename='CNN-model.pt')"
   ]
  }
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